Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning | Paper Citation Record

Observation 2ad0cb59-4410-4e54-b507-44763ec6efce · outbound

This paper cites Prototype-sample relation distillation: towards replay-free continual learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Prototype-sample relation distillation: towards replay-free continual learning

Reference 1

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This paper cites Beyond Supervised Continual Learning: a Review.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Beyond Supervised Continual Learning: a Review

Reference 2

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Observation de981298-fa4d-4d7e-8779-ef7b52bc13c3 · outbound

This paper cites Rainbow memory: Continual learning with a memory of diverse samples.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Rainbow memory: Continual learning with a memory of diverse samples

Reference 3

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Observation b46319ca-0dde-4a73-8c83-f9c5a5844fe0 · outbound

This paper cites Continual semi-supervised learning through contrastive interpolation consistency.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Continual semi-supervised learning through contrastive interpolation consistency

Reference 4

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This paper cites Hypernetworks for Continual Semi-Supervised Learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Hypernetworks for Continual Semi-Supervised Learning

Reference 5

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This paper cites Emerg- ing properties in self-supervised vision transformers.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Emerg- ing properties in self-supervised vision transformers

Reference 6

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Observation cf94e10e-3a88-471c-8e0b-d1da0631df1e · outbound

This paper cites Efficient lifelong learning with a- gem.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Efficient lifelong learning with a- gem

Reference 7

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Observation 10253379-4b88-4f61-8d8c-56537ac8bf6f · outbound

This paper cites Softmatch: Addressing the quantity-quality tradeoff in semi- supervised learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Softmatch: Addressing the quantity-quality tradeoff in semi- supervised learning

Reference 8

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Observation 11023e61-9d9d-49c2-a81a-542ded699e48 · outbound

This paper cites Pg-lbo: Enhancing high-dimensional bayesian optimization with pseudo-label and gaussian process guid- ance.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Pg-lbo: Enhancing high-dimensional bayesian optimization with pseudo-label and gaussian process guid- ance

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.745307Z

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Observation 6d111280-49b8-4555-a6c1-fd673ce5e430 · outbound

This paper cites Boosting semi- supervised learning by exploiting all unlabeled data.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Boosting semi- supervised learning by exploiting all unlabeled data

Reference 10

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Observation f9fb164d-e408-429d-9ae4-72a43c7faf0c · outbound

This paper cites Semi-supervised few-shot class-incremental learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Semi-supervised few-shot class-incremental learning

Reference 11

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Observation 9f743df7-1da2-4f60-b0ff-c2ce1d3b7292 · outbound

This paper cites Uncertainty-guided semi- supervised few-shot class-incremental learning with knowl- edge distillation.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Uncertainty-guided semi- supervised few-shot class-incremental learning with knowl- edge distillation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.712307Z

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Observation 8db39f87-449a-4920-bf19-ada8fbc6d856 · outbound

This paper cites Uncertainty-aware distillation for semi-supervised few-shot class-incremental learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Uncertainty-aware distillation for semi-supervised few-shot class-incremental learning

Reference 13

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This paper cites Continual pro- totype evolution: Learning online from non-stationary data streams.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Continual pro- totype evolution: Learning online from non-stationary data streams

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.690043Z

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Observation 29c2b612-bb69-4652-a715-41ae2ee8cf72 · outbound

This paper cites Towards semi-supervised learning with non- random missing labels.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Towards semi-supervised learning with non- random missing labels

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.679728Z

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Observation 6c89d7e4-9c0b-40a3-9f22-309e1eca8cd6 · outbound

This paper cites Mutexmatch: Semi- supervised learning with mutex-based consistency regular- ization.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Mutexmatch: Semi- supervised learning with mutex-based consistency regular- ization

Reference 16

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Observation 2ea45881-14d7-4c5f-b120-3121f6c124ed · outbound

This paper cites Roll with the punches: Expansion and shrinkage of soft label selection for semi-supervised fine- grained learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Roll with the punches: Expansion and shrinkage of soft label selection for semi-supervised fine- grained learning

Reference 17

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This paper cites Dy- namic sub-graph distillation for robust semi-supervised con- tinual learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Dy- namic sub-graph distillation for robust semi-supervised con- tinual learning

Reference 18

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Observation 04d5a7db-cf2d-4538-ba57-8c08d4b8d182 · outbound

This paper cites Ddgr: Continual learning with deep diffusion-based generative replay.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Ddgr: Continual learning with deep diffusion-based generative replay

Reference 19

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This paper cites A survey on semi-supervised learning for delayed partially la- belled data streams.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning A survey on semi-supervised learning for delayed partially la- belled data streams

Reference 20

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This paper cites Deep residual learning for image recognition.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Deep residual learning for image recognition

Reference 21

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Observation 6c5e2324-f543-4dfa-92a5-7b4b2f060d79 · outbound

This paper cites Class-incremental learning using diffusion model for distillation and replay.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Class-incremental learning using diffusion model for distillation and replay

Reference 22

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This paper cites Class- incremental learning by knowledge distillation with adaptive feature consolidation.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Class- incremental learning by knowledge distillation with adaptive feature consolidation

Reference 23

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This paper cites A soft nearest-neighbor framework for con- tinual semi-supervised learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning A soft nearest-neighbor framework for con- tinual semi-supervised learning

Reference 24

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Observation b90c8fd3-8c39-4734-961b-d2c1e8644019 · outbound

This paper cites Achieving a better stability-plasticity trade-off via auxiliary networks in continual learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Achieving a better stability-plasticity trade-off via auxiliary networks in continual learning

Reference 25

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This paper cites Bal- ancing stability and plasticity through advanced null space in continual learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Bal- ancing stability and plasticity through advanced null space in continual learning

Reference 26

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This paper cites Learning multiple layers of features from tiny images.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Learning multiple layers of features from tiny images

Reference 27

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This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

Reference 28

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This paper cites Do pre-trained models benefit equally in continual learning? In IEEE/CVF Winter Conference on Applications of Computer Vision, 2023.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Do pre-trained models benefit equally in continual learning? In IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Reference 29

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This paper cites Learn to grow: A continual structure learn- ing framework for overcoming catastrophic forgetting.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Learn to grow: A continual structure learn- ing framework for overcoming catastrophic forgetting

Reference 30

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This paper cites Model behavior preserving for class-incremental learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Model behavior preserving for class-incremental learning

Reference 31

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Observation 1c51ea8e-6a2b-41c4-b4ac-d2526f325a5f · outbound

This paper cites Augmented geometric distillation for data-free incremental person reid.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Augmented geometric distillation for data-free incremental person reid

Reference 32

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This paper cites Learning to predict gradients for semi-supervised con- tinual learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Learning to predict gradients for semi-supervised con- tinual learning

Reference 33

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This paper cites Metric learning for large scale image clas- sification: Generalizing to new classes at near-zero cost.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Metric learning for large scale image clas- sification: Generalizing to new classes at near-zero cost

Reference 34

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This paper cites Virtual adversarial training: a regularization method for supervised and semi-supervised learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Virtual adversarial training: a regularization method for supervised and semi-supervised learning

Reference 35

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This paper cites An Overview of Deep Semi-Supervised Learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning An Overview of Deep Semi-Supervised Learning

Reference 36

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This paper cites Class- incremental learning for action recognition in videos.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Class- incremental learning for action recognition in videos

Reference 37

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Observation 8b560521-b24b-4d5c-b813-7259c7404565 · outbound

This paper cites Class-incremental learn- ing with pre-allocated fixed classifiers.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Class-incremental learn- ing with pre-allocated fixed classifiers

Reference 38

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Observation 6bba499c-44cf-4166-a97b-b51052eef8f6 · outbound

This paper cites Fetril: Feature translation for exemplar-free class-incremental learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Fetril: Feature translation for exemplar-free class-incremental learning

Reference 39

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This paper cites Learn- ing transferable visual models from natural language super- vision.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Learn- ing transferable visual models from natural language super- vision

Reference 40

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This paper cites icarl: Incremental clas- sifier and representation learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning icarl: Incremental clas- sifier and representation learning

Reference 41

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This paper cites Memory-efficient semi-supervised continual learning: The world is its own replay buffer.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Memory-efficient semi-supervised continual learning: The world is its own replay buffer

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.394482Z

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Observation eeb724f9-c0cc-4bf9-bf86-5bb4d4b0b6a5 · outbound

This paper cites Always be dreaming: A new approach for data-free class-incremental learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Always be dreaming: A new approach for data-free class-incremental learning

Reference 43

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Observation 2c49b1ca-ea97-445f-9868-0dce8e17a016 · outbound

This paper cites Coda-prompt: Contin- ual decomposed attention-based prompting for rehearsal-free continual learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Coda-prompt: Contin- ual decomposed attention-based prompting for rehearsal-free continual learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.373361Z

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Observation 70c3f6e7-3057-4a92-a24b-dfb8e55f04fb · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.362923Z

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Observation c3d468f6-9485-47b3-b690-e52900e8f6b5 · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.352262Z

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Observation 2f6ef771-8060-4427-8a90-fb564e422a4d · outbound

This paper cites Con- trastive multiview coding.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Con- trastive multiview coding

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.341493Z

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Observation d93800e0-137f-4782-9f62-af59d42cfd85 · outbound

This paper cites Foster: Feature boosting and compression for class- incremental learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Foster: Feature boosting and compression for class- incremental learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.331251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 435aba4b-aa03-414a-8e95-84ae9b4d1052 · outbound

This paper cites Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.320732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b4517d1a-62a8-4b27-8927-d02caa5027f9 · outbound

This paper cites A comprehensive survey of continual learning: theory, method and application.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning A comprehensive survey of continual learning: theory, method and application

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.309701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2e130fa3-8287-4992-973f-edbf62b2aec8 · outbound

This paper cites Freematch: Self-adaptive thresholding for semi-supervised learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Freematch: Self-adaptive thresholding for semi-supervised learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.299281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f4443490-1b64-4cad-a4b4-533a43ddb6df · outbound

This paper cites Learning to prompt for continual learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Learning to prompt for continual learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.288377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 276190b3-f1bf-4fb3-a4e4-2d71d762f282 · outbound

This paper cites Continual learning: A review of techniques, challenges and future directions.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Continual learning: A review of techniques, challenges and future directions

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.276985Z

Source-reported events for the cited work

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Observation e74c7480-9478-450b-b096-9a8882e9b0b6 · outbound

This paper cites Self-training with noisy student improves imagenet clas- sification.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Self-training with noisy student improves imagenet clas- sification

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.266133Z

Source-reported events for the cited work

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Observation 21058819-e851-4b1e-89cd-d6ee1fb74fe4 · outbound

This paper cites Der: Dynam- ically expandable representation for class incremental learn- ing.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Der: Dynam- ically expandable representation for class incremental learn- ing

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.255267Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c5d5f732-6341-4fb4-ae0d-e8ee2d3b8fee · outbound

This paper cites A survey on deep semi-supervised learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning A survey on deep semi-supervised learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.244737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8f145a18-aff7-4a21-a4e4-d9c3949b9044 · outbound

This paper cites Neural collapse inspired feature- classifier alignment for few-shot class-incremental learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Neural collapse inspired feature- classifier alignment for few-shot class-incremental learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.234025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 86e6e568-5dbe-468b-860d-e2e988f40412 · outbound

This paper cites Few-shot incremental learning with contin- ually evolved classifiers.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Few-shot incremental learning with contin- ually evolved classifiers

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.223353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5b382471-5318-48d7-895c-c189b74eaa08 · outbound

This paper cites Class-incremental learning via deep model consolidation.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Class-incremental learning via deep model consolidation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.212347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aab30bf5-3dc6-4f63-894a-881433e0b063 · outbound

This paper cites Memory-efficient class-incremental learning for image classification.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Memory-efficient class-incremental learning for image classification

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.201374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8f7aef85-6cc9-4ab2-85e1-9067c9b7d8e0 · outbound

This paper cites Simmatch: Semi-supervised learning with similarity matching.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Simmatch: Semi-supervised learning with similarity matching

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.189576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 511edd17-fbd8-4d1e-bf24-4137b282d35a · outbound

This paper cites Simmatchv2: Semi- supervised learning with graph consistency.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Simmatchv2: Semi- supervised learning with graph consistency

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.177783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 23151867-42a8-4853-946b-23cebfc55904 · outbound

This paper cites Forward compatible few- shot class-incremental learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Forward compatible few- shot class-incremental learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.166910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 715cb51d-a726-42d8-9d9a-d9791af9df55 · outbound

This paper cites Class-incremental learning: A survey.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Class-incremental learning: A survey

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.155201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 71b67049-0ef9-4e11-8cab-169560c75897 · outbound

This paper cites Self-promoted prototype refinement for few-shot class- incremental learning.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Self-promoted prototype refinement for few-shot class- incremental learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:28:12.144101Z

Source-reported events for the cited work

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